Efficient Web Browsing with Semantic Annotation: A Case Study of Product Images in E-Commerce Sites

Web browsing task is based on depth-first searching scheme, so that searching relevant information from Web may be very tedious. In this paper, we propose personal browsing assistant system based on user intentions modeling. Before explicitly requested by a user, this system can analyze the prefetched resources from the hyperlinked Webpages and compare them with the estimated user intention, so that it can help him to make a better decision like which Webpage should be requested next. More important problem is the semantic heterogeneity between Web spaces. It makes the understandability of locally annotated resources more difficult. We apply semantic annotation, which is a transcoding procedure with the global ontology. Therefore, each local metadata can be semantically enriched, and efficiently comparable. As testing bed of our experiment, we organized three different online clothes stores whose images are annotated by semantically heterogeneous metadata. We simulated virtual customers navigating these cyberspaces. According to the predefined preferences of customer models, they conducted comparison-shopping. We have shown the reasonability of supporting the Web browsing, and its performance was evaluated as measuring the total size of browsed hyperspace.

[1]  Matthias Klusch,et al.  Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet , 1999 .

[2]  Thomas R. Gruber,et al.  A Translation Approach to Portable Ontologies , 1993 .

[3]  Detmar W. Straub,et al.  A conceptual investigation of the e-commerce industry , 2000, CACM.

[4]  Ching-chih Chen,et al.  Automated semantic annotation and retrieval based on sharable ontology and case-based learning techniques , 2003, 2003 Joint Conference on Digital Libraries, 2003. Proceedings..

[5]  Tsukasa Hirashima,et al.  Context-sensitive filtering for browsing in hypertext , 1998, IUI '98.

[6]  Carole A. Goble,et al.  Towards Annotation Using DAML+OIL , 2001, Semannot@K-CAP 2001.

[7]  Arthur Stutt,et al.  Alice: Assisting Online Shoppers through Ontologies and Novel Interface Metaphors , 2002, EKAW.

[8]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[9]  Marc A. Smith,et al.  AURA: A mobile platform for object and location annotation , 2003 .

[10]  Steffen Staab,et al.  Measuring Similarity between Ontologies , 2002, EKAW.

[11]  Laura M. Haas,et al.  Clio: a semi-automatic tool for schema mapping , 2001, SIGMOD '01.

[12]  R. Manmatha,et al.  Automatic image annotation and retrieval using cross-media relevance models , 2003, SIGIR.

[13]  Marja-Riitta Koivunen,et al.  Annotea: an open RDF infrastructure for shared Web annotations , 2001, WWW '01.

[14]  Brendan Kitts,et al.  Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities , 2000, KDD '00.

[15]  Michael Gertz,et al.  Annotating scientific images: a concept-based approach , 2002, Proceedings 14th International Conference on Scientific and Statistical Database Management.

[16]  Pattie Maes,et al.  Agents that reduce work and information overload , 1994, CACM.

[17]  Djemel Ziou,et al.  Image Retrieval from the World Wide Web: Issues, Techniques, and Systems , 2004, CSUR.

[18]  Nir Vulkan.  Economic Implications of Agent Technology and E-Commerce , 1999 .

[19]  Alexiei Dingli,et al.  User-System Cooperation in Document Annotation Based on Information Extraction , 2002, EKAW.

[20]  Steffen Staab,et al.  S-CREAM: Semiautomatic CREAtion of Metadata , 2002, SAAKM@ECAI.

[21]  Steffen Staab,et al.  S-CREAM: Semiautomatic CREAtion of Metadata , 2002, SAAKM@ECAI.

[22]  George Karypis,et al.  Evaluation of Item-Based Top-N Recommendation Algorithms , 2001, CIKM '01.